A predictive energy management strategy considering travel route information is proposed to explore the energy-saving potential of plug-in hybrid electric vehicles. The extreme learning machine is used as a short-term speed predictor, and the battery temperature is added as an optimization term to the cost function. By comparing the training data sets, it is found that using the real-world historical speed information for training can achieve higher prediction accuracy than using typical standard driving cycles. The speed predictor trained based on the data considering travel route information can further improve the prediction accuracy. The impact of battery temperature on the total cost is also analyzed. By adjusting the temperature weighting coefficient of the battery, a balance between economy and battery aging can be achieved. In addition, it is found that the ambient temperature also affects vehicular energy consumption. Finally, the proposed method is compared with PMP, MPC, and CD-CS methods, showing its effectiveness and practicability.

中文翻译：

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插电式混合动力汽车的自然数据驱动的预测能源管理

为了探索插电式混合动力汽车的节能潜力，提出了一种基于出行路线信息的预测性能源管理策略。极限学习机用作短期速度预测器，电池温度作为优化项添加到成本函数中。通过比较训练数据集，发现使用真实世界的历史速度信息进行训练比使用典型的标准驾驶周期可以获得更高的预测精度。基于考虑了行驶路线信息的数据训练的速度预测器可以进一步提高预测精度。还分析了电池温度对总成本的影响。通过调节电池的温度加权系数，可以实现经济性与电池老化之间的平衡。此外，发现环境温度也影响车辆能量消耗。最后，将该方法与PMP，MPC和CD-CS方法进行了比较，证明了其有效性和实用性。